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linted tmle_core (#39)
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@ronikobrosly
ronikobrosly authored Apr 25, 2021
1 parent f240019 commit eb0e826
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Showing 6 changed files with 93 additions and 91 deletions.
2 changes: 1 addition & 1 deletion causal_curve/core.py
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Expand Up @@ -12,7 +12,7 @@ class Core:
def __init__(self):
pass

__version__ = "1.0.5"
__version__ = "1.0.6"

def get_params(self):
"""Returns a dict of all of the object's user-facing parameters
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1 change: 0 additions & 1 deletion causal_curve/gps_core.py
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Expand Up @@ -9,7 +9,6 @@
import pandas as pd
from pandas.api.types import is_float_dtype, is_integer_dtype, is_numeric_dtype
from pygam import LinearGAM, LogisticGAM, s
from scipy.special import logit
from scipy.stats import gamma, norm
import statsmodels.api as sm
from statsmodels.genmod.families.links import inverse_power as Inverse_Power
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173 changes: 86 additions & 87 deletions causal_curve/tmle_core.py
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Expand Up @@ -118,7 +118,8 @@ class TMLE_Core(Core):
----------
Kennedy EH, Ma Z, McHugh MD, Small DS. Nonparametric methods for doubly robust estimation
of continuous treatment effects. Journal of the Royal Statistical Society, Series B. 79(4), 2017, pp.1229-1245.
of continuous treatment effects. Journal of the Royal Statistical Society,
Series B. 79(4), 2017, pp.1229-1245.
van der Laan MJ and Rubin D. Targeted maximum likelihood learning. In: The International
Journal of Biostatistics, 2(1), 2006.
Expand Down Expand Up @@ -362,33 +363,45 @@ def fit(self, T, X, y):

# Produce expanded versions of the inputs
self.if_verbose_print("Transforming data for the Q-model and G-model")
self.grid_values, self.fully_expanded_x , self.fully_expanded_t_and_x = self._transform_inputs()
(
self.grid_values,
self.fully_expanded_x,
self.fully_expanded_t_and_x,
) = self._transform_inputs()

# Fit G-model and get relevent predictions
self.if_verbose_print("Fitting G-model and making treatment assignment predictions...")
self.if_verbose_print(
"Fitting G-model and making treatment assignment predictions..."
)
self.g_model_preds, self.g_model_2_preds = self._g_model()

# Fit Q-model and get relevent predictions
self.if_verbose_print("Fitting Q-model and making outcome predictions...")
self.q_model_preds = self._q_model()

# Calculating treatment assignment adjustment using G-model's predictions
self.if_verbose_print("Calculating treatment assignment adjustment using G-model's predictions...")
self.n_interpd_values, self.var_n_interpd_values = self._treatment_assignment_correction()
self.if_verbose_print(
"Calculating treatment assignment adjustment using G-model's predictions..."
)
(
self.n_interpd_values,
self.var_n_interpd_values,
) = self._treatment_assignment_correction()

# Adjusting outcome using Q-model's predictions
self.if_verbose_print("Adjusting outcome using Q-model's predictions...")
self.outcome_adjust, self.expand_outcome_adjust = self._outcome_adjustment()

# Calculating corrected pseudo-outcome values
self.if_verbose_print("Calculating corrected pseudo-outcome values...")
self.pseudo_out = (self.y_data - self.outcome_adjust) / (self.n_interpd_values / self.var_n_interpd_values) + self.expand_outcome_adjust
self.pseudo_out = (self.y_data - self.outcome_adjust) / (
self.n_interpd_values / self.var_n_interpd_values
) + self.expand_outcome_adjust

# Training final GAM model using pseudo-outcome values
self.if_verbose_print("Training final GAM model using pseudo-outcome values...")
self.final_gam = self._fit_final_gam()


def calculate_CDRC(self, ci=0.95):
"""Using the results of the fitted model, this generates a dataframe of CDRC point estimates
at each of the values of the treatment grid. Connecting these estimates will produce
Expand All @@ -413,7 +426,8 @@ def calculate_CDRC(self, ci=0.95):

self._validate_calculate_CDRC_params(ci)

self.if_verbose_print("""
self.if_verbose_print(
"""
Generating predictions for each value of treatment grid,
and averaging to get the CDRC..."""
)
Expand All @@ -423,47 +437,42 @@ def calculate_CDRC(self, ci=0.95):
self._cdrc_preds = self._cdrc_predictions_continuous(ci)

return pd.DataFrame(
self._cdrc_preds, columns=["Treatment", "Causal_Dose_Response", "Lower_CI", "Upper_CI"]
self._cdrc_preds,
columns=["Treatment", "Causal_Dose_Response", "Lower_CI", "Upper_CI"],
).round(3)


def _transform_inputs(self):
"""Takes the treatment and covariates and transforms so they can
be used by the algo"""

# Create treatment grid
grid_values = np.linspace(
start=self.t_data.min(),
stop=self.t_data.max(),
num=self.treatment_grid_num
start=self.t_data.min(), stop=self.t_data.max(), num=self.treatment_grid_num
)

# Create expanded treatment array
expanded_t = np.array([])
for treat_value in grid_values:
expanded_t = np.append(expanded_t, ([treat_value] * self.num_rows))
expanded_t = np.append(expanded_t, ([treat_value] * self.num_rows))

# Create expanded treatment array with covariates
expanded_t_and_x = pd.concat(
[
pd.DataFrame(expanded_t),
pd.concat(
[self.x_data] * self.treatment_grid_num
).reset_index(drop = True, inplace = False),
pd.concat([self.x_data] * self.treatment_grid_num).reset_index(
drop=True, inplace=False
),
],
axis = 1,
ignore_index = True
axis=1,
ignore_index=True,
)

expanded_t_and_x.columns = [self.treatment_col_name] + self.covariate_col_names

fully_expanded_t_and_x = pd.concat(
[
pd.concat([self.x_data, self.t_data], axis=1),
expanded_t_and_x
],
axis = 0,
ignore_index = True
[pd.concat([self.x_data, self.t_data], axis=1), expanded_t_and_x],
axis=0,
ignore_index=True,
)

fully_expanded_x = fully_expanded_t_and_x[self.covariate_col_names]
Expand All @@ -480,16 +489,16 @@ def _g_model(self):
n_estimators=self.n_estimators,
max_depth=self.max_depth,
learning_rate=self.learning_rate,
random_state=self.random_seed
).fit(X = X, y = t)
random_state=self.random_seed,
).fit(X=X, y=t)
g_model_preds = g_model.predict(self.fully_expanded_x)

g_model2 = GradientBoostingRegressor(
n_estimators=self.n_estimators,
max_depth=self.max_depth,
learning_rate=self.learning_rate,
random_state=self.random_seed
).fit(X = X, y = ((t - g_model_preds[0:self.num_rows])**2))
random_state=self.random_seed,
).fit(X=X, y=((t - g_model_preds[0 : self.num_rows]) ** 2))
g_model_2_preds = g_model2.predict(self.fully_expanded_x)

return g_model_preds, g_model_2_preds
Expand All @@ -506,105 +515,95 @@ def _q_model(self):
n_estimators=self.n_estimators,
max_depth=self.max_depth,
learning_rate=self.learning_rate,
random_state=self.random_seed
).fit(X = X, y = y)
random_state=self.random_seed,
).fit(X=X, y=y)
q_model_preds = q_model.predict(self.fully_expanded_t_and_x)

return q_model_preds


def _treatment_assignment_correction(self):
"""Uses the G-model and its predictions to adjust treatment assignment
"""
"""Uses the G-model and its predictions to adjust treatment assignment"""

t_standard = (
(self.fully_expanded_t_and_x[self.treatment_col_name] - self.g_model_preds) / np.sqrt(self.g_model_2_preds)
self.fully_expanded_t_and_x[self.treatment_col_name] - self.g_model_preds
) / np.sqrt(self.g_model_2_preds)

interpd_values = (
interp1d(
self.one_dim_estimate_density(t_standard.values)[0],
self.one_dim_estimate_density(t_standard.values[0 : self.num_rows])[1],
kind="linear",
)(t_standard)
/ np.sqrt(self.g_model_2_preds)
)

interpd_values = interp1d(
self.one_dim_estimate_density(t_standard.values)[0],
self.one_dim_estimate_density(t_standard.values[0:self.num_rows])[1],
kind='linear'
)(t_standard) / np.sqrt(self.g_model_2_preds)
n_interpd_values = interpd_values[0 : self.num_rows]

n_interpd_values = interpd_values[0:self.num_rows]

temp_interpd = interpd_values[self.num_rows:]
temp_interpd = interpd_values[self.num_rows :]

zeros_mat = np.zeros((self.num_rows, self.treatment_grid_num))

for i in range(0, self.treatment_grid_num):
lower = i * self.num_rows
upper = i * self.num_rows + self.num_rows
zeros_mat[:,i] = temp_interpd[lower:upper]
lower = i * self.num_rows
upper = i * self.num_rows + self.num_rows
zeros_mat[:, i] = temp_interpd[lower:upper]

var_n_interpd_values = self.pred_from_loess(
train_x = self.grid_values,
train_y = zeros_mat.mean(axis = 0),
x_to_pred = self.t_data
train_x=self.grid_values,
train_y=zeros_mat.mean(axis=0),
x_to_pred=self.t_data,
)

return n_interpd_values, var_n_interpd_values


def _outcome_adjustment(self):
"""Uses the Q-model and its predictions to adjust the outcome
"""
"""Uses the Q-model and its predictions to adjust the outcome"""

outcome_adjust = self.q_model_preds[0:self.num_rows]
outcome_adjust = self.q_model_preds[0 : self.num_rows]

temp_outcome_adjust = self.q_model_preds[self.num_rows:]
temp_outcome_adjust = self.q_model_preds[self.num_rows :]

zero_mat = np.zeros((self.num_rows, self.treatment_grid_num))
for i in range(0, self.treatment_grid_num):
lower = i * self.num_rows
upper = i * self.num_rows + self.num_rows
zero_mat[:,i] = temp_outcome_adjust[lower:upper]
lower = i * self.num_rows
upper = i * self.num_rows + self.num_rows
zero_mat[:, i] = temp_outcome_adjust[lower:upper]

expand_outcome_adjust = self.pred_from_loess(
train_x = self.grid_values,
train_y = zero_mat.mean(axis = 0),
x_to_pred = self.t_data
train_x=self.grid_values,
train_y=zero_mat.mean(axis=0),
x_to_pred=self.t_data,
)

return outcome_adjust, expand_outcome_adjust

def _fit_final_gam(self):
"""We now regress the original treatment values against the pseudo-outcome values
"""
"""We now regress the original treatment values against the pseudo-outcome values"""

return LinearGAM(
s(0, n_splines=30, spline_order=3),
max_iter=500,
lam=self.bandwidth
).fit(self.t_data, y = self.pseudo_out)
s(0, n_splines=30, spline_order=3), max_iter=500, lam=self.bandwidth
).fit(self.t_data, y=self.pseudo_out)

def one_dim_estimate_density(self, series):
"""
Takes in a numpy array, returns grid values for KDE and predicted probabilities
"""
series_grid = np.linspace(
start=series.min(),
stop=series.max(),
num=self.num_rows
"""
Takes in a numpy array, returns grid values for KDE and predicted probabilities
"""
series_grid = np.linspace(
start=series.min(), stop=series.max(), num=self.num_rows
)

kde = KernelDensity(
kernel='gaussian',
bandwidth=self.bandwidth
).fit(series.reshape(-1, 1))
kde = KernelDensity(kernel="gaussian", bandwidth=self.bandwidth).fit(
series.reshape(-1, 1)
)

return series_grid, np.exp(kde.score_samples(series_grid.reshape(-1, 1)))
return series_grid, np.exp(kde.score_samples(series_grid.reshape(-1, 1)))

def pred_from_loess(self, train_x, train_y, x_to_pred):
"""
Trains simple loess regression and returns predictions
"""
kr_model = KernelReg(
endog = train_y,
exog = train_x,
var_type = 'c',
bw = [self.bandwidth]
"""
Trains simple loess regression and returns predictions
"""
kr_model = KernelReg(
endog=train_y, exog=train_x, var_type="c", bw=[self.bandwidth]
)

return kr_model.fit(x_to_pred)[0]
return kr_model.fit(x_to_pred)[0]
4 changes: 4 additions & 0 deletions docs/changelog.rst
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Expand Up @@ -4,6 +4,10 @@
Change Log
==========

Version 1.0.6
-------------
- Latest version of python black can now run. Linted tmle_core.py.

Version 1.0.5
-------------
- Removed `master` branch, replaced with `main`
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2 changes: 1 addition & 1 deletion docs/conf.py
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Expand Up @@ -23,7 +23,7 @@
author = "Roni Kobrosly"

# The full version, including alpha/beta/rc tags
release = "1.0.5"
release = "1.0.6"

# -- General configuration ---------------------------------------------------

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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -5,7 +5,7 @@

setuptools.setup(
name="causal-curve",
version="1.0.5",
version="1.0.6",
author="Roni Kobrosly",
author_email="[email protected]",
description="A python library with tools to perform causal inference using \
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